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Particle Swarm Optimization for Nano-Particles Extraction from Supporting Materials
Mohamed abd-ElRahman Abdou
Pages - 361 - 370     |    Revised - 01-07-2011     |    Published - 05-08-2011
Volume - 5   Issue - 3    |    Publication Date - July / August 2011  Table of Contents
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KEYWORDS
TEM Image Scaning, Particle Swarm Optimization, Image Segmentation, Nano-particles Characterization
ABSTRACT
Metallic and non-metallic nano-particles have attracted much interest concerning their wide applications. Transmission electron microscopy (TEM) is the state of the art method to characterize a nano-particle with respect to size, morphology, structure, or composition. This paper presents an efficient evolutionary computational method, particle swarm optimization (PSO), for automatic segmentation of nano-particles. A threshold-based segmentation technique is applied, where image entropy is attacked as a minimization problem to specify local and global thresholds. We are concerned with reducing wrong characterization of nano-particles due to concentration of liquid solutions or supporting material within the acquired image. The obtained results are compared with manual techniques and with previous researches in this area.
1 Google Scholar 
2 CiteSeerX 
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4 SlideShare 
5 PdfSR 
1 C.A. Pena-Reyes, M. Sipper. “Evolving fuzzy rules for breast cancer diagnosis”. In Proceedings of 1998 International Symposium on Nonlinear Theory and Applications (NOLTA’98), 2: 369–372,1998.
2 C.A. Pena-Reyes, M. Sipper. “A fuzzy-genetic approach to breast cancer diagnosis”. Artificial Intelligence in Medicine, 17: 131–155, 1999.
3 X. Chang, J.H. Lilly. “Evolutionary design of a fuzzy classifier from data”. IEEE Transactions on Systems, Man, and Cybernetics, 34: 1894–1906, 2004.
4 R. Setiono. “Extracting rules from pruned neural networks for breast cancer diagnosis”. Artificial Intelligence in Medicine, 8: 37–51, 1996.
5 R. Setiono, H. Liu. “Symbolic representation of neural networks”. Computer, 29: 71–77, 1996.
6 K.P. Bennett, O.L. Mangasarian. “Neural network training via linear programming”. Advances in Optimization and Parallel Computing, 56–67, 1992.
7 Pei-ChannChang, Jyun-JieLin, Chen-HaoLiu. Computer methods and Programs in Biomedicine,Vol.(not yet): 2011.
8 L.Zhang, T.Mei, Y.Liu, D.Tao, H.Zhou. “Visual search reranking via adaptive particle swarm optimization”. Pattern Recognition, 44: 1811- 1820, 2001.
9 C.C. Bojarczuk, H.S. Lopes, A.A. Freitas. “Genetic programming for knowledge discovery in chestpain diagnosis: exploring a promising data mining approach”. IEEE Engineering in Medicine and Biology Magazine 19: 38–44, 2000.
10 Du Feng, Shi Wenkang, Chen Liangzhou, Deng Yong, Zhu Zhenfu. “Infrared image segmentation with 2-D maximum entropy method based on particle swarm optimization (PSO)”. Pattern Recognition Letters, 26: 597–603, 2005.
11 J Kennedy, R. Eberhart. “Particle Swarm Optimization”. 1942–1948, (1995).
12 M.A. Abdou, Bayumy B.A. Youssef, W.M. Sheta. “Nano-particle Characterization Using a Fast Hybrid Clustering Technique for TEM Images”. (IJCSIS) International Journal of Computer Science and Information Security, 8 (9): 101-110, 2010.
13 T.Chem, Tubitak, H.Woehrle, E.Hutchison, S.Ozkar, G.Finke. “Analysis of Nanoparticle Transmission Electron Microscopy Data Using a Public- Domain Image-Processing Program”.Image Metrology, 30: 1-13, 2006.
14 N.R.Pal, S.K.Pal, Object-background segmentation using new definitions of entropy, Proc. Inst.Elec. Eng. 136, pp.284-295, 1989.
15 G.J.Klir, T.Folger. “Fuzzy Sets Uncertainty and Information”. Printice Hall, Englewood Cliffs, NJ,(1988).
Dr. Mohamed abd-ElRahman Abdou
Informatics Research Institute - Egypt
m.abdou@pua.edu.eg